a quantitative error map generation for modi cation of ... · jagran lakecity university, bhopal,...

A Quantitative Error Map Generation forModification of Manual Socket for Below Knee

Amputee

Arun Dayal Udai∗ and Amarendra Nath Sinha#

∗Department of Mechanical Engineering,Birla Institute of Technology,

Mesra, Ranchi - [email protected]

http://bitmesra.ac.in

#Director, School of Engineering and TechnologyJagran Lakecity University, Bhopal,

[email protected]

https://www.jlu.edu.in

Abstract. Traditional prosthetic socket manufactured through manualplaster-casting method for lower limb prosthesis consists of geometricalerrors since the shape of the cast changes due to hand impressions, pres-sure involved while taking out the negative cast and final during handsculpting of the positive cast. As the Plaster of Paris (PoP) model is fur-ther utilized for manufacturing of prosthetic limb, the error gets carriedover to the prosthetic limb, which results in improper fit of the artifi-cial limb and ultimately results wounds to the amputees residual limb,particularly at the stress prone knee. In the present work, an error mapgeneration using Reverse Engineering (RE) technique has been proposedwhich assists to rectify the manually prepared PoP model. The correc-tion technique is quantitative and requires less hand sculpting skills ofthe prosthetist. With this process, the accuracy increases to the extentof product that is being manufactured by CNC/RP method and at com-paratively much cheaper rate.

Keywords: Reverse Engineering, Prosthetic Socket, CAD

1 Introduction

The process of developing of artificial human limbs for below knee (BK) am-putees has nearly come up to the level when it is easily being manufacturedthrough manual as well as advanced CAD/CAM techniques. The research workstill has scope to achieve desired accuracy in the custom fit design as well as newinnovation in data acquisition and process refinement for its production whereit nearly matches the CAD data. Such a design process is expected to improvethe quality, shorten lead time, reduce cost and improve data acquisition process.

2 Arun Dayal Udai∗ and Amarendra Nath Sinha#

The overall process should be simple so that technicians can be easily trainedand wide spread in the developing countries. Several attempts are made by re-searchers to implement some of the stated qualities of a custom fit artificial limbranging from data acquisition process to the manufacturing process. Some of theworks improves the data acquisition process while a few of them refines the postprocessing methods of the cloud data to generate a 3D CAD model. Kurt Oberget. al. [5] discusses a stump measuring system and socket mold is prepared usingNC milling machine and CAD workstation. A 3D surface scanner for lower limbprosthetics is discussed by [1] that uses four CID cameras and three white lightprojectors. Nicolas E. Walsh, et. al. [9] developed a complete system for manu-facturing prostheses using RE.The conventional manual manufacturing of artificial limbs by first developingnegative and positive cast of residual limbs does not give accuracy which ulti-mately results in pain and difficulty to amputees putting artificial limbs on hisresidual part of live limb [3]. Both measurement and manual manufacturing re-sults to inaccuracy because of irregular shape and size of the amputees residuallimb. RE is utilized by [4] for CAD model preparation of customized artifi-cial joint, which used a Coordinate Measuring Machine (CMM). An alternativemeans of 3D reconstruction is attempted by [6] using Computerized Tomogra-phy (CT) scanning, image processing and RE technique. A similar process isutilized by [2] using spiral X-Ray CT scanning and a different CAD package forsurface development. In the present work a process is developed which uses aRE technique to prepare a quantitative error map which is utilized to modifythe manually prepared Plaster of Paris (PoP) model of the amputees residuallimb. The modified PoP model is utilized to manufacture the fiber reinforcedcomposite socket which is to be fitted to the manually prepared artificial limb.This ongoing multistage endeavor involves acquisition of cloud points throughnon-contact white light 3D scanner, development of wire frame model and fi-nally the surface model of the residual part of the limb as well as the manuallyprepared PoP cast. The processes of RE followed in steps are preparing theresidual limb surface, fixing registration marks, 3D scanning, cloud registration& alignment, data filtering, segmentation, curve fitting to build wire frame ge-ometry and building of the surface model. These steps thoroughly applied withfew innovative techniques and modification to the traditional techniques in use.Each subsequent transformation, like cloud points to curve and curves to surfaceare analyzed and modified iteratively, to obtain a final shape with least possibleerror. The surface model for both the PoP cast and the live residual limb isaligned together and an error map is prepared. This process supplements tradi-tional method of manufacturing artificial limb with more quantitative techniquesfor rectification of artificial limb.

2 Methodology

The error map generation procedure involves a) digitizing the surfaces of am-putee limb and the manually prepared positive plaster model, b) generating 3D

A Quantitative Error Map Generation... 3

surface model for both the digitized data set using RE method, c) aligning thesurface model to a common axis and d) generating a quantitative error data set(a color coded three dimensional image) with reference to a geometrical positionon plaster model. UGS Imageware R⃝ 12.0 is used for the above all processes as itis widely available and easier for any less skilled prosthetists to learn. As RE be-ing more of an art work than an engineering it depends very much on individualskill. The method discussed quantifies the prosthetists job with a more scien-tific data and supplements the existing methods for the modification of manualartificial sockets.

2.1 Digitizing Surfaces.

The amputees residual limb consists of soft tissues, uneven surfaces and texture,that makes it difficult to scan using most precise white light scanner. So, thesurfaces were sprayed with a non toxic white color and scanned with a minimumpossible number of views in very short interval, within which, the limb remainsvirtually stable over the fixture. The cloud points were obtained at constantroom temperature of 25◦C, so as to have an uniform optical properties of theair medium. Proper registration marks were placed at appropriate locations (asshown in Fig. 1), which assist in alignment of multiple view cloud sets and inreferencing of error map over the plaster model in later stages of the process.Each registration mark was made visible from at least two views of the scanner.This made it easier to identify any particular view cloud data during alignmentof all the cloud point to a single coordinate system.The cloud points for the limb and the plaster model were followed with cordial

Fig. 1. Scanning process with registration marks.

deviation and space sampling method for data reduction, smoothening and fil-tering of any redundant points. Similar procedure was adapted for scanning of


the manually prepared plaster model. The completed cloud points as shown inFig. 2, of the plaster and live limb was processed using Imageware R⃝ package toform surface models of each in standard RE steps of segmentation, curve fittingand surface generation.Geometrical feature based cloud segmentation method was adapted, which seg-

Fig. 2. Completed scanned cloud data of amputee’s residual limb.

regates the tubular cloud data along the length of the limb and the dome shapedclouds near to the lower end of the limb. Any remaining shapes was classified astransition shapes which was covered with edge bounded surface patches.

2.2 Generating 3D Surface model using RE technique.

Along the tubular cloud data a curve aligned 30 − 40 transverse sections wasmade, which was fitted with closed B-Spline curves as discussed in [8]. This isshown in Fig. 3. Curves being the base for surface generation, more effort was

Fig. 3. Set of closed B-Spline curves fitted over sectioned cloud points.

made at the curves level to rectify any possible chances of surface error. Thestart points of all the curves were aligned and curve parameters were made toflow in same direction. Any unnecessary control points were removed to a certaintolerance and the knots are redistributed uniformly along the length of the curve.These set of curves were utilized to generate a tubular loft surface. The domeshaped caps at the end of the lower limb was fitted with loft surfaces as shown


(a) Surfacemodel of livelimb

(b) Surfacemodel of plastercast

Fig. 4. Surface Models of live limb and plaster cast

in Fig. 4. At each stage of cloud to curve and curve to surface generation, thedeviation from the original cloud was inspected, and the curves or surfaces wererectified to diminish the error within permissible limits. Modifications ware madeby moving the control points normal to the curves or surface. A first order andsecond order of continuity was established between any joining surfaces as in[7]. Surface models for the live limb and the plaster casting was generated withsimilar steps. As the cloud points for plaster was more uniform, the surfacesgenerated were also smoother.

2.3 Aligning the Surfaces to a Common axis

Axis of the prepared CAD Surface model of the live limb or the plaster wasderived from the best fit line through the centers of the circle fitted over thetransverse slices, along the length of the completed scanned cloud data. Theaxis may also be formed with the centroid of the sectioned cloud data. But theformer method was adopted as it is simple and also has the desired accuracy.The axes along with the surfaces for live limb and the plaster model was broughttogether to form a single axis. Now, fixing one of the surfaces (live model in thecurrent case) the other one was moved linearly along the axis and rotated aboutthe axis to obtain a best fit through and automated iterative process. Some ofthe intermediate steps are shown in Fig. 5. The other Degrees of Freedom (DOF)for the moving cloud was constrained.

2.4 Generating Quantitative Error map and its Implementation

Considering the live model as the standard reference the deviation of plastermodel surface was inspected. A three dimensional color plot was obtained whichshows the maximum and minimum deviations from the standard reference. The


(a) Initial import (b) Axis alignment (c) Rotating for best fit

Fig. 5. Alignment of surfaces along the common axis

distance between the surfaces was calculated, based on the surface normal tothe reference surface to the plaster model surface. The deviations correspondingto any location was outlined for manual modification to the plaster cast. Thelocation on the plaster model corresponding to any location on its CAD counter-part was found with respect to the registration points fixed while scanning. Thedeviations were marked on the plaster and outlined for manual modification. Inautomated process the plaster model may be directly modified on CAD platformand manufactured using NC, CAM or any Rapid Prototyping system.In the current work a manually modified plaster cast was taken for socket prepa-ration using fiber reinforced epoxy resin socket. The original plaster cast was lostwhile taking off the socket. However, the record may be maintained on any CADdatabase.

3 Results and Discussions

Surfaces for the CAD model was compared with the original cloud obtainedafter scanning and 90% of the surfaces were within ±3.0 mm of deviation whichwas manually corrected up to ±0.5mm accuracy. A slope variation betweenthe surfaces joined below 5◦ was accepted. A maximum gap of 0.05mm existedbetween the surface joints. Volume measurements of the solid models were foundto correspond within 3 − 5% of surface models and direct determinations wasmade using Archimedean weighing, (as in [2]), higher being the live limb error.Such a model can be exported to any automated manufacturing machine likeRP or a CNC Machine. The surface difference was within limits of −1.34mm to+0.69mm over the working area of the sockets as shown in Fig. 6.Two below knee amputee cases were fully rehabilitated with manually preparedartificial limbs socket modified using the current technique. This is shown inFig. 7. Figure 7(a) shows the exoskeleton and Fig. 7(b) shows the endoskeletontype of artificial limb structure. The method not only supplements the existingmanual plaster casting technique with a quantitative method for modificationbut also eliminates skill dependency or any art work of prosthetist, with minor


(a) Error-map front view (b) Error-map top view

Fig. 6. Color coded Error Map

(a) Artificial leg offirst case

(b) Artificial leg ofsecond case

Fig. 7. Prepared artificial legs with the modified sockets

additions to the infrastructure cost of the CAD systems. The proposed method isexpected to be useful for clinical evaluation, recordkeeping, education/researchand transferring manual modification skills to CAD system. As the work has beencarried out for below knee amputee cases only, the discussions are made withreference to BK amputee. This may be extended for any above knee amputee orhand amputation case also.

4 Conclusions

The paper presents a methodolgy to enhance the precision of a prosthetic socketmanufactured using hand sculpting technique. The proposed method was demon-


strated using a below knee amputee case. However, this may be extended for anyother amputee cases as well. The paper used a CAD based reverse engineeringtechnique to quantify the errors existing on a hand sculpted PoP cast and gener-ated an error map that may be used to enhance the precision of the final socketmanufactured out of it.

Acknowledgments: This work was carried out with the financial support fromDST, Govt. of India, through one of its project at BIT, Mesra, Ranchi. Theauthors would also like to thank District Disabled Rehabilitation Center, Ranchifor their support during fabrication of the artificial limb. Scientists at CMERI,Durgapur is also acknowledged for their contrubutions to obtain the cloud data.

References

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2. Bhatia, Gulab H., Commean, Paul K., Smith, Kirk E., Vannier, Michael W.: Au-tomated lower limb prosthesis design, In: Proceedings of SPIE, Visualization inBiomedical Computing, Vol. 2359, pp. 493–503 (1994)

3. Cochrane, H., Orsi, K., Reilly, P.: Lower Limb Amputation, Part 3: Prosthetics a10 Year Literature Review. J. Prosthetics and Orthotics 25, 21–28 (2001)

4. Lin, Y. P., Wang, C. T., Dai, K. R.: Reverse engineering in CAD model reconstruc-tion of customized artificial joint. J. of Medical Engineering and Physics, Elsevier,27(2), 189–193 (2005)

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6. Shuxian, Z., Wanhua, Z., Bingheng, L.: 3D reconstruction of the structure of a resid-ual limb for customising the design of a prosthetic socket. J. of Medical Engineeringand Physics, Elsevier, 27(1), 67–74 (2005)

7. Sinha, A. N., Udai, A. D.: Development of Artificial Limb Surface using ReverseEngineering Method, In: Proceedings of International Conference on Advances inMachine Design & Industry Automation (ICAMDIA 2007), College of Engineering,Pune, India, Online: http://web.iitd.ernet.in/~mez108088/files/sinha_udai_icamdia_2007.pdf, pp. 519–522 (2007)

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